Last updated: 2020-08-06

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No Parkland Meshblock Summary

  df_mesh_lockdown_summary_all %>%
    filter(as.numeric(area)==0) %>%
  { . } -> df_none

df_none %>%
  count( MB_CATEGORY_NAME_2016, sort=TRUE) %>%
  gt::gt() %>%
  gt::tab_header(
           title = glue::glue("Meshblocks with no parks within {max_distance}km"),
           subtitle = "Number by category"
)
Meshblocks with no parks within 1km
Number by category
MB_CATEGORY_NAME_2016 n
Residential 581
Primary Production 407
Other 225
Industrial 134
Commercial 28
Education 18
Water 3
Transport 2
Hospital/Medical 1 
df_none %>%
  select(-starts_with('mc'), -State) %>%
  summarise( across( where(is.numeric),
                    .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum))) %>%
  gt::gt() %>%
gt::tab_header(
               title = glue::glue("Meshblocks with no parks within {max_distance}km"),
               subtitle = "Summary statistics"
)
Meshblocks with no parks within 1km
Summary statistics
area_mean area_max area_min area_sd area_total AREA_ALBERS_SQKM_mean AREA_ALBERS_SQKM_max AREA_ALBERS_SQKM_min AREA_ALBERS_SQKM_sd AREA_ALBERS_SQKM_total Dwelling_mean Dwelling_max Dwelling_min Dwelling_sd Dwelling_total Person_mean Person_max Person_min Person_sd Person_total
0 [m^2] 0 [m^2] 0 [m^2] 0 0 [m^2] 2.635717 57.1033 0.0033 4.619509 3687.369 29.62545 424 0 27.47099 41446 78.95568 1145 0 75.12695 110459

LGA level summary for Residential Meshblocks

df_mesh_lockdown_summary_residential  %>%
  group_by(lga_name) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_lga_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_lga_summary %>%
  datatable(options = list(pageLength = 10))
df_mesh_lockdown_summary_all  %>%
  write_csv('output/lockdown_greenspace_mesh_detail.csv')

df_lga_summary %>%
  write_csv('output/lockdown_greenspace_lga_summary.csv')

SA2 level summary for Residential Meshblocks

df_mesh_lockdown_summary_residential  %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  group_by(SA2_MAIN16) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  right_join( df_mesh_lockdown_summary_all %>% distinct(SA2_MAIN16), by ='SA2_MAIN16' ) %>%
  { . } -> df_sa2_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa2_summary %>%
  head(10) %>%
  gt::gt() %>%
gt::tab_header(
               title = "SA2 Summary",
               subtitle = "First 10 rows"
)
SA2 Summary
First 10 rows
SA2_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
204011061 1.7169276 [km^2] 2.3370023 [km^2] 1.39489599 [km^2] 0.5371344 5.150783 [km^2] 0.23680000 0.4042 0.0977 0.155197390 0.7104 17.00000 23 10 6.557439 51 28.00000 37 16 10.81665 84 3
206011105 0.2751499 [km^2] 1.0575642 [km^2] 0.00000000 [km^2] 0.2739687 71.814123 [km^2] 0.01345326 0.0351 0.0015 0.006225212 3.5113 42.68199 177 5 22.246746 11140 88.89655 345 7 40.75226 23202 261
206011106 0.3642722 [km^2] 0.7031185 [km^2] 0.08279169 [km^2] 0.1847112 42.255576 [km^2] 0.01409828 0.0370 0.0017 0.006733059 1.6354 48.14655 443 0 56.793639 5585 93.51724 678 4 81.06857 10848 116
206011107 0.4471127 [km^2] 0.9509893 [km^2] 0.04471476 [km^2] 0.1913856 72.432265 [km^2] 0.01666852 0.0531 0.0015 0.008884808 2.7003 40.65432 217 10 19.390182 6586 82.62346 385 15 38.58459 13385 162
206011108 0.2166905 [km^2] 0.8985413 [km^2] 0.01428953 [km^2] 0.2226937 54.822697 [km^2] 0.02256957 0.0946 0.0027 0.009758878 5.7101 42.08696 241 0 19.585596 10648 101.04743 394 0 38.73718 25565 253
206011109 0.2000361 [km^2] 0.4129230 [km^2] 0.04271871 [km^2] 0.1082549 17.403144 [km^2] 0.02919425 0.0508 0.0081 0.008556411 2.5399 42.85057 72 17 11.069053 3728 111.44828 217 24 33.06363 9696 87
206021110 0.3810333 [km^2] 0.9168410 [km^2] 0.10983105 [km^2] 0.1829141 38.484362 [km^2] 0.02071881 0.0452 0.0057 0.008669299 2.0926 38.26733 75 13 11.290608 3865 87.00000 193 32 28.69286 8787 101
206021111 0.4036037 [km^2] 1.1249674 [km^2] 0.12612263 [km^2] 0.2038735 110.183807 [km^2] 0.01700879 0.0604 0.0017 0.007729779 4.6434 38.27106 134 3 15.577362 10448 86.81319 248 7 36.36293 23700 273
206021112 0.2486049 [km^2] 0.6556514 [km^2] 0.02532690 [km^2] 0.1642283 53.947261 [km^2] 0.01750968 0.0444 0.0026 0.007826259 3.7996 38.51613 98 11 11.424915 8358 82.70046 257 19 31.50966 17946 217
206031113 0.3554608 [km^2] 1.2379420 [km^2] 0.01026676 [km^2] 0.3504621 51.541810 [km^2] 0.01984138 0.0606 0.0025 0.009725815 2.8770 43.61379 210 12 23.884614 6324 96.86207 451 36 47.87623 14045 145 
df_sa2_summary %>%
  write_csv('output/lockdown_greenspace_sa2_summary.csv')

SA1 level summary

df_mesh_lockdown_summary_residential  %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  group_by(SA1_MAIN16) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  right_join( df_mesh_lockdown_summary_all %>% distinct(SA1_MAIN16), by ='SA1_MAIN16' ) %>%
  { . } -> df_sa1_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa1_summary %>%
  head(10) %>%
  gt::gt() %>%
  gt::tab_header(
                 title = "SA1 Summary",
                 subtitle = "First 10 rows"
  )
SA1 Summary
First 10 rows
SA1_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
0.304391939 [km^2] 0.30439194 [km^2] 3.043919e-01 [km^2] NA 0.30439194 [km^2] 0.0266000 0.0266 0.0266 NA 0.0266 48.00 48 48 NA 48 83.0000 83 83 NA 83 1
20401106101 1.716927629 [km^2] 2.33700232 [km^2] 1.394896e+00 [km^2] 0.537134365 5.15078289 [km^2] 0.2368000 0.4042 0.0977 0.155197390 0.7104 17.00 23 10 6.557439 51 28.0000 37 16 10.816654 84 3
20601110501 0.236750338 [km^2] 0.28897998 [km^2] 1.845207e-01 [km^2] 0.073863871 0.47350068 [km^2] 0.0205000 0.0229 0.0181 0.003394113 0.0410 54.50 59 50 6.363961 109 109.5000 115 104 7.778175 219 2
20601110502 0.009053943 [km^2] 0.03324198 [km^2] 8.786882e-05 [km^2] 0.011126021 0.07243154 [km^2] 0.0129125 0.0214 0.0050 0.005480729 0.1033 30.75 40 15 7.535630 246 72.7500 106 44 23.407874 582 8
20601110503 0.111808495 [km^2] 0.14763699 [km^2] 7.592192e-02 [km^2] 0.032314674 0.55904248 [km^2] 0.0124400 0.0174 0.0096 0.002926260 0.0622 38.00 53 27 9.643651 190 89.6000 154 69 36.239481 448 5
20601110504 0.150157289 [km^2] 0.15946122 [km^2] 1.334812e-01 [km^2] 0.014474176 0.45047187 [km^2] 0.0168000 0.0226 0.0094 0.006743886 0.0504 45.00 52 38 7.000000 135 100.3333 138 60 39.068316 301 3
20601110505 0.075412195 [km^2] 0.10663823 [km^2] 4.236977e-02 [km^2] 0.030155196 0.30164878 [km^2] 0.0171250 0.0218 0.0121 0.004618351 0.0685 40.50 50 30 9.146948 162 89.0000 113 60 21.924112 356 4
20601110506 0.031269759 [km^2] 0.04784777 [km^2] 1.238339e-02 [km^2] 0.013764637 0.15634879 [km^2] 0.0139600 0.0188 0.0073 0.004723135 0.0698 34.20 51 25 10.034939 171 78.4000 92 55 14.135063 392 5
20601110507 0.054184880 [km^2] 0.06144133 [km^2] 4.540331e-02 [km^2] 0.006063374 0.27092440 [km^2] 0.0165800 0.0236 0.0089 0.006171467 0.0829 46.20 59 34 9.418068 231 100.0000 130 69 26.804850 500 5
20601110508 0.100350967 [km^2] 0.15275205 [km^2] 5.873237e-02 [km^2] 0.036874938 0.50175483 [km^2] 0.0125000 0.0165 0.0086 0.003248846 0.0625 39.80 53 23 10.825895 199 75.8000 86 54 13.084342 379 5 
df_sa1_summary %>%
  write_csv('output/lockdown_greenspace_sa1_summary.csv')

Map

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle(glue::glue('Km^2 greenspace access within {max_distance}km of meshblock average for SA1'))

Version Author Date
824aa81 Dennis Wollersheim 2020-08-06
878b52c Dennis Wollersheim 2020-08-06
4550ce3 Dennis Wollersheim 2020-08-06
0f2b938 Dennis Wollersheim 2020-08-06
e1388a3 Dennis Wollersheim 2020-08-06
a71385f Dennis Wollersheim 2020-08-05
b83cc3d Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05 
map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle(glue::glue('Km^2 greenspace access within {max_distance}km of meshblock average for SA2'))

Version Author Date
824aa81 Dennis Wollersheim 2020-08-06
878b52c Dennis Wollersheim 2020-08-06
4550ce3 Dennis Wollersheim 2020-08-06
0f2b938 Dennis Wollersheim 2020-08-06
e1388a3 Dennis Wollersheim 2020-08-06

Green Map

map_mesh %>%
  inner_join(df_mesh_lockdown_summary_parks, by = "MB_CODE16" ) %>%
  { . } -> df_parks

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  geom_sf(data=df_parks, fill=RColorBrewer::brewer.pal(3,'Greens')[2]) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle(glue::glue('Km^2 greenspace access within {max_distance}km of meshblock. Green is parks'))

Version Author Date
824aa81 Dennis Wollersheim 2020-08-06
878b52c Dennis Wollersheim 2020-08-06
4550ce3 Dennis Wollersheim 2020-08-06
0f2b938 Dennis Wollersheim 2020-08-06
e1388a3 Dennis Wollersheim 2020-08-06
a71385f Dennis Wollersheim 2020-08-05
b83cc3d Dennis Wollersheim 2020-08-05
a69a370 Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05 
map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  geom_sf(data=df_parks, fill=RColorBrewer::brewer.pal(3,'Greens')[2]) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle(glue::glue('Km^2 greenspace access within {max_distance}km of meshblock. Green is parks'))

Version Author Date
824aa81 Dennis Wollersheim 2020-08-06
878b52c Dennis Wollersheim 2020-08-06
4550ce3 Dennis Wollersheim 2020-08-06
0f2b938 Dennis Wollersheim 2020-08-06
e1388a3 Dennis Wollersheim 2020-08-06

Interactive Map SA2 level

map_breaks = 0:5/5 * max_distance^2
map_levels = paste0( map_breaks, '-', map_breaks + map_breaks[2] - map_breaks[1], 'km' )
map_levels [[length(map_levels)-1]] = paste0('>', map_breaks[[ length(map_breaks)-1]], 'km' )
map_levels = map_levels [1:length(map_levels)-1]
map_breaks[[length(map_breaks)]] = 3.2*max_distance^2


map_colors = RColorBrewer::brewer.pal(6,'YlOrRd')[2:6]
map_palette = colorFactor(map_colors, levels=map_levels)

map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  mutate( area_factor = cut( area.mean, breaks=map_breaks,
                            labels= map_levels )) %>%
  { . } -> df_leaflet


df_leaflet %>%
  leaflet(height=2000, width=2000) %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
              color = ~map_palette(area_factor)) %>%
  addLegend("bottomright", pal = map_palette, values = ~area_factor,
            title = glue::glue("Mean park area within {max_distance}km circle of mesh blocks within SA2"),
            opacity = 1
            ) %>%
  addPolygons( weight = .5, smoothFactor = 0.5,
              opacity = .5, fillOpacity = 0.0,
              data = map_lockdown_lga) %>%
  addPolygons( weight = 1, smoothFactor = 0.5,
              opacity = 1, fillOpacity = 1,
              color = RColorBrewer::brewer.pal(3,'Greens')[2],
              data = df_parks)
Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Interactive Map SA1 levelk

map_colors = RColorBrewer::brewer.pal(6,'YlOrRd')[2:6]
map_palette = colorFactor(map_colors, levels=map_levels)

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  mutate( area_factor = cut( area.mean, breaks=map_breaks,
                            labels= map_levels )) %>%
  { . } -> df_leaflet


df_leaflet %>%
  leaflet(height=2000, width=2000) %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
              color = ~map_palette(area_factor)) %>%
  addLegend("bottomright", pal = map_palette, values = ~area_factor,
            title = glue::glue("Mean park area within {max_distance}km circle of mesh blocks within SA2"),
            opacity = 1
  ) %>%
  addPolygons( weight = .5, smoothFactor = 0.5,
              opacity = .5, fillOpacity = 0.0,
              data = map_lockdown_lga) %>%
  addPolygons( weight = 1, smoothFactor = 0.5,
              opacity = 1, fillOpacity = 1,
              color = RColorBrewer::brewer.pal(3,'Greens')[2],
              data = df_parks)
Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rvest_0.3.5        xml2_1.3.2         DT_0.13            leaflet_2.0.3.9000
 [5] workflowr_1.6.2    cleangeo_0.2-3     maptools_1.0-1     rgeos_0.5-3       
 [9] sp_1.4-2           geohash_0.4.0      osrm_3.3.3         sf_0.9-3          
[13] janitor_2.0.1      rmarkdown_2.1      lubridate_1.7.8    geosphere_1.5-10  
[17] forcats_0.5.0      stringr_1.4.0      dplyr_1.0.0        purrr_0.3.4       
[21] readr_1.3.1        tidyr_1.1.0        tibble_3.0.3       ggplot2_3.3.2     
[25] tidyverse_1.3.0    drake_7.12.4       dotenv_1.0.2       conflicted_1.0.4  
[29] nvimcom_0.9-99    

loaded via a namespace (and not attached):
 [1] nlme_3.1-147       fs_1.4.1           RColorBrewer_1.1-2 filelock_1.0.2    
 [5] progress_1.2.2     httr_1.4.1         rprojroot_1.3-2    tools_4.0.2       
 [9] backports_1.1.8    R6_2.4.1           KernSmooth_2.23-17 DBI_1.1.0         
[13] colorspace_1.4-1   withr_2.2.0        tidyselect_1.1.0   prettyunits_1.1.1 
[17] compiler_4.0.2     git2r_0.27.1       Xmisc_0.2.1        cli_2.0.2         
[21] gt_0.2.1           labeling_0.3       sass_0.2.0         checkmate_2.0.0   
[25] scales_1.1.1       classInt_0.4-3     digest_0.6.25      foreign_0.8-79    
[29] txtq_0.2.3         pkgconfig_2.0.3    htmltools_0.5.0    dbplyr_1.4.3      
[33] htmlwidgets_1.5.1  rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11   
[37] farver_2.0.3       generics_0.0.2     jsonlite_1.7.0     crosstalk_1.1.0.1 
[41] magrittr_1.5       Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1       
[45] lifecycle_0.2.0    stringi_1.4.6      whisker_0.4        yaml_2.2.1        
[49] snakecase_0.11.0   storr_1.2.1        grid_4.0.2         parallel_4.0.2    
[53] promises_1.1.1     crayon_1.3.4       lattice_0.20-41    haven_2.2.0       
[57] hms_0.5.3          knitr_1.28         pillar_1.4.6       igraph_1.2.5      
[61] base64url_1.4      reprex_0.3.0       glue_1.4.1         evaluate_0.14     
[65] modelr_0.1.7       vctrs_0.3.2        httpuv_1.5.4       cellranger_1.1.0  
[69] gtable_0.3.0       assertthat_0.2.1   xfun_0.16          broom_0.5.6       
[73] e1071_1.7-3        later_1.1.0.1      viridisLite_0.3.0  class_7.3-17      
[77] memoise_1.1.0      units_0.6-6        ellipsis_0.3.1